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优化的比较蛋白质组学方法在神经退行性疾病研究中的应用。

An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research.

机构信息

The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK.

The Euan MacDonald Centre for Motor Neuron Disease Research, The University of Edinburgh, Edinburgh EH9 9AG, UK.

出版信息

Cells. 2022 Aug 26;11(17):2653. doi: 10.3390/cells11172653.

Abstract

Recent advances in proteomic technologies now allow unparalleled assessment of the molecular composition of a wide range of sample types. However, the application of such technologies and techniques should not be undertaken lightly. Here, we describe why the design of a proteomics experiment itself is only the first step in yielding high-quality, translatable results. Indeed, the effectiveness and/or impact of the majority of contemporary proteomics screens are hindered not by commonly considered technical limitations such as low proteome coverage but rather by insufficient analyses. Proteomic experimentation requires a careful methodological selection to account for variables from sample collection, through to database searches for peptide identification to standardised post-mass spectrometry options directed analysis workflow, which should be adjusted for each study, from determining when and how to filter proteomic data to choosing holistic versus trend-wise analyses for biologically relevant patterns. Finally, we highlight and discuss the difficulties inherent in the modelling and study of the majority of progressive neurodegenerative conditions. We provide evidence (in the context of neurodegenerative research) for the benefit of undertaking a comparative approach through the application of the above considerations in the alignment of publicly available pre-existing data sets to identify potential novel regulators of neuronal stability.

摘要

近年来蛋白质组学技术的进步使得对广泛样本类型的分子组成进行前所未有的评估成为可能。然而,这种技术和方法的应用不应轻率进行。在这里,我们描述了为什么蛋白质组学实验的设计本身只是产生高质量、可转化结果的第一步。事实上,大多数当代蛋白质组学筛选的有效性和/或影响并不是受到通常认为的技术限制(例如低蛋白质组覆盖率)的阻碍,而是由于分析不足。蛋白质组学实验需要仔细选择方法,以考虑从样本收集到肽鉴定数据库搜索的各种变量,再到标准化的质谱后选项直接分析工作流程,这应根据每个研究进行调整,从确定何时以及如何过滤蛋白质组数据到选择整体分析还是趋势分析来寻找与生物学相关的模式。最后,我们强调并讨论了在建模和研究大多数进行性神经退行性疾病时固有的困难。我们提供了证据(在神经退行性研究的背景下),证明通过应用上述考虑因素对齐公开可用的现有数据集来识别神经元稳定性的潜在新调节剂,可以采取比较方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4dd/9454658/4dceb252eec0/cells-11-02653-g0A1.jpg

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